Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
This addresses depression diagnosis for mental health applications, but it is incremental as it builds on existing hierarchical networks with a conditioning mechanism.
The authors tackled depression detection from transcribed clinical interviews by augmenting a Hierarchical Attention Network with affective conditioning on linguistic features, achieving state-of-the-art F1 scores of 71.6 and 68.6 on two datasets.
In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject's mood but also the use of language. To this end we use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica. Our analysis shows that individuals diagnosed with depression use affective language to a greater extent than not-depressed. Our experiments show that external affective information improves the performance of the proposed architecture in the General Psychotherapy Corpus and the DAIC-WoZ 2017 depression datasets, achieving state-of-the-art 71.6 and 68.6 F1 scores respectively.